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Axiomatic decomposition of a zero-sum game: the penalty shoot-out case

机译:零和游戏的公理分解:惩罚爆炸案例

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The game of soccer has offered matter of wide scientific analysis about the effective application of the game theory in real-life. The field observations have often detected divergent behaviors from theoretical predictions. The basic problem comes from the fact that it is difficult to build scientific models reflecting reality as closely as possible. Axiomatic Design offers us a powerful tool of rational decomposition of a real and complex issue into elementary components. Independence Axiom guarantees that game decomposition will define a set of elementary actions logically consistent and free of redundancies. At the same time, Information Axiom can allow to select among alternative strategies, those that they predict the actions with a higher probability rate of success. In this paper, it is suggested the use of the Axiomatic Design methodology in the Collectively Exhaustive and Mutually Exclusive (CEME) mode, as a tool of analysis of the penalty shoot-out in extra time. This methodology allows to define the game strategies for goalkeepers and penalty takers. It will be analyzed both, the case when the opponents' behavior is well known and the situation when the statistics about the opponents are unknown. Axiomatic Design allows the process of decomposition to be simplified, enabling the selection of optimal game strategies. These strategies correspond to Nash’s equilibrium solutions when you already know about your opponents' game behavior. On the contrary, when penalty takers whose behavior is unknown, then it is always possible to define a strategy corresponding to the Bayesian equilibrium game solutions.
机译:足球比赛对博弈论在现实生活中的有效应用方面提供了广泛的科学分析。现场观察经常检测到理论预测中的不同行为。基本问题来自这一事实,即难以建立尽可能紧密地反映现实的科学模型。公理设计为我们提供了一个强大的理性分解工具,对基本组件进行了真实和复杂的问题。独立公理保证游戏分解将定义一组逻辑上的初级操作,并没有冗余。同时,信息公理可以允许选择替代策略,它们预测具有更高概率的成功率的动作。在本文中,建议在额外的额外时间内使用分析和相互排斥(CEME)模式的公理设计方法。该方法允许为守门员和惩罚者定义游戏策略。它将被分析,这种情况,当对手的行为是众所周知的,并且当对对手的统计数据未知时的情况。公理设计允许简化分解过程,从而实现最佳游戏策略。当您已经了解对手的游戏行为时,这些策略对应于NASH的均衡解决方案。相反,当行为未知的惩罚者时,那么始终可以定义与贝叶斯均衡游戏解决方案相对应的策略。

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